Variance of Level 2 random effect depending on value of variable in highest level (Level 3) in Hierarchical model

I was reading material on hierarchical models, especially 3 level hierarchical models. One of the examples I found was having the hierarchy Hospitals-Wards-Nurses. Only 1 measurement per Nurse. So when I think of a random intercept only model for this hierarchy I was using a distribution for hospitals and then different distributions for wards in different hospitals. i.e. Effect of hospital ~ $N(0,\sigma_{hosp}^2)$ and effect of ward in hospital h are distributed as ~ $N(0, \sigma_{wards-hosp-h}^2)$. So depending on the hospital the variance of the wards is different.

I found in Multilevel Analysis by Joop J. Hox (pg. 34 to 36) that only 1 variance for all the wards is considered. i.e. effect of any ward ~ $N(0, \sigma_{ward}^2)$. I further checked SAS multilevel model primer(pg. 15) that SAS also does the same.

My question is why would we assume that irrespective of the hospital the effect of any ward is ~ $N(0, \sigma_{ward}^2)$? If I am not understanding incorrectly, then using different variances of wards per hospitals means that I am only modeling variances of wards for the hospital in the current data set. But then I also sample the variances of these wards from another distribution (I follow a Bayesian approach). So if a new hospital comes up I could give some interval estimate of what the variance of the ward effect for this hospital will be.

Am I thinking in right direction? and under what circumstances would someone do the simplification which was done in the book and SAS primer?